{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting NYC Taxi Fares with Small Dataset"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screenshot%202021-10-05%20121806.jpg](attachment:Screenshot%202021-10-05%20121806.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a notebook originally written for Rapids but converted to plain Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import socket, time\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "\n",
    "# To install Holoviews and hvplot\n",
    "# conda install -c conda-forge holoviews\n",
    "# conda install -c pyviz hvplot\n",
    "import holoviews as hv\n",
    "from holoviews import opts\n",
    "import numpy as np\n",
    "import hvplot.pandas\n",
    "\n",
    "hv.extension(\"bokeh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspecting the Data\n",
    "\n",
    "We'll use Pandas to load and parse one CSV file from each year into a DataFrame. It makes it 3 files overall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = \"/yellow-taxi/\"\n",
    "\n",
    "df_2014 = pd.read_csv(\n",
    "    base_path + \"2014/yellow_tripdata_2014-01.csv\",\n",
    "    parse_dates=[\" pickup_datetime\", \" dropoff_datetime\"],\n",
    ")\n",
    "df_2014.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleanup\n",
    "\n",
    "As usual, the data needs to be massaged a bit before we can start adding features that are useful to an ML model.\n",
    "\n",
    "For example, in the 2014 taxi CSV files, there are `pickup_datetime` and `dropoff_datetime` columns. The 2015 CSVs have `tpep_pickup_datetime` and `tpep_dropoff_datetime`, which are the same columns. One year has `rate_code`, and another `RateCodeID`.\n",
    "\n",
    "Also, some CSV files have column names with extraneous spaces in them.\n",
    "\n",
    "Worst of all, starting in the July 2016 CSVs, pickup & dropoff latitude and longitude data were replaced by location IDs, making the second half of the year useless to us.\n",
    "\n",
    "We'll do a little string manipulation, column renaming, and concatenating of DataFrames to sidestep the problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary of required columns and their datatypes\n",
    "must_haves = {\n",
    "    \"pickup_datetime\": \"datetime64[s]\",\n",
    "    \"dropoff_datetime\": \"datetime64[s]\",\n",
    "    \"passenger_count\": \"int32\",\n",
    "    \"trip_distance\": \"float32\",\n",
    "    \"pickup_longitude\": \"float32\",\n",
    "    \"pickup_latitude\": \"float32\",\n",
    "    \"rate_code\": \"int32\",\n",
    "    \"dropoff_longitude\": \"float32\",\n",
    "    \"dropoff_latitude\": \"float32\",\n",
    "    \"fare_amount\": \"float32\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean(ddf, must_haves):\n",
    "    # replace the extraneous spaces in column names and lower the font type\n",
    "    tmp = {col: col.strip().lower() for col in list(ddf.columns)}\n",
    "    ddf = ddf.rename(columns=tmp)\n",
    "\n",
    "    ddf = ddf.rename(\n",
    "        columns={\n",
    "            \"tpep_pickup_datetime\": \"pickup_datetime\",\n",
    "            \"tpep_dropoff_datetime\": \"dropoff_datetime\",\n",
    "            \"ratecodeid\": \"rate_code\",\n",
    "        }\n",
    "    )\n",
    "\n",
    "    for col in ddf.columns:\n",
    "        if col not in must_haves:\n",
    "            ddf = ddf.drop(columns=col)\n",
    "            continue\n",
    "        if ddf[col].dtype == \"object\":\n",
    "            ddf[col] = ddf[col].fillna(\"-1\")\n",
    "\n",
    "    return ddf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> NOTE: </b>We will realize that some of 2015 data has column name as `RateCodeID` and others have `RatecodeID`. When we rename the columns in the clean function, it internally doesn't pass meta while calling map_partitions(). This leads to the error of column name mismatch in the returned data. For this reason, we will call the clean function with map_partition and pass the meta to it. Here is the link to the bug created for that: https://github.com/rapidsai/cudf/issues/5413 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2014 = clean(df_2014, must_haves)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still have 2015 and the first half of 2016's data to read and clean. Let's increase our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2015 = pd.read_csv(\n",
    "    base_path + \"2015/yellow_tripdata_2015-01.csv\",\n",
    "    parse_dates=[\"tpep_pickup_datetime\", \"tpep_dropoff_datetime\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2015 = clean(df_2015, must_haves)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling 2016's Mid-Year Schema Change"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2016, only January - June CSVs have the columns we need. If we try to read base_path+2016/yellow_*.csv, Dask will not appreciate having differing schemas in the same DataFrame.\n",
    "\n",
    "Instead, we'll need to create a list of the valid months and read them independently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "months = [str(x).rjust(2, \"0\") for x in range(1, 7)]\n",
    "valid_files = [\n",
    "    base_path + \"2016/yellow_tripdata_2016-\" + month + \".csv\" for month in months\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read & clean 2016 data and concat all DFs\n",
    "df_2016 = clean(\n",
    "    pd.read_csv(\n",
    "        valid_files[0], parse_dates=[\"tpep_pickup_datetime\", \"tpep_dropoff_datetime\"]\n",
    "    ),\n",
    "    must_haves,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatenate multiple DataFrames into one bigger one\n",
    "taxi_df = pd.concat([df_2014, df_2015, df_2016])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# taxi_df = taxi_df.persist()\n",
    "taxi_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we are checking out if there are any non-sensical records and outliers, and in such case, we need to remove them from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check out if there is any negative total trip time\n",
    "taxi_df[taxi_df.dropoff_datetime <= taxi_df.pickup_datetime].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check out if there is any abnormal data where trip distance is short, but the fare is very high.\n",
    "taxi_df[(taxi_df.trip_distance < 10) & (taxi_df.fare_amount > 300)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check out if there is any abnormal data where trip distance is long, but the fare is very low.\n",
    "taxi_df[(taxi_df.trip_distance > 50) & (taxi_df.fare_amount < 50)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using only 2016-01 data for visuals.\n",
    "# taxi_df_cdf = clean(cudf.read_csv(valid_files[0]),must_haves)\n",
    "\n",
    "# Using entire 2016 data for visualization\n",
    "taxi_df_cdf = taxi_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot below visualizes the histogram of trip_distance and we can see some abnormal trip_distance values for some records. Taking this and also the NYC map coordinates into consideration, we will only select records where tripdistance < 500 miles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# Histogram using cupy and Holoviews\n",
    "# frequencies, edges = cupy.histogram(x=cupy.array(taxi_df_cdf[\"trip_distance\"]) , bins=20)\n",
    "# hist = hv.Histogram((np.array(edges.tolist()), np.array(frequencies.tolist())))\n",
    "\n",
    "# Histogram using hvplot\n",
    "hist = taxi_df_cdf.hvplot.hist(\"trip_distance\", bins=20, bin_range=(0, 10))\n",
    "\n",
    "# Customizing the plot\n",
    "hist.opts(\n",
    "    xlabel=\"trip distance (miles)\", ylabel=\"count\", color=\"green\", width=900, height=400\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, the plot below visualizes the histogram of fare_amount and we can see some abnormal fare_amount values for some records. Taking this and also the NYC map coordinates into consideration, we will only select records where fare_amount < 500$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# Histogram using cupy and Holoviews\n",
    "# frequencies, edges = cupy.histogram(x=cupy.array(taxi_df_cdf[\"fare_amount\"]) , bins=20)\n",
    "# hist = hv.Histogram((np.array(edges.tolist()), np.array(frequencies.tolist())))\n",
    "\n",
    "# Histogram using hvplot\n",
    "hist = taxi_df_cdf.hvplot.hist(\"fare_amount\", bins=20, bin_range=(0, 50))\n",
    "\n",
    "# Customizing the plot\n",
    "hist.opts(xlabel=\"fare amount ($)\", ylabel=\"count\", color=\"green\", width=900, height=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# Plot the number of passengers per trip. We'll remove the records where passenger_count > 5.\n",
    "# Plotting using Holoviews\n",
    "# bar = hv.Bars(taxi_df_cdf.groupby(\"passenger_count\").size().to_frame().rename(columns={0:\"count\"}))\n",
    "\n",
    "# Plotting using hvplot\n",
    "df_bar = (\n",
    "    taxi_df_cdf.groupby(\"passenger_count\")\n",
    "    .size()\n",
    "    .to_frame()\n",
    "    .rename(columns={0: \"count\"})\n",
    "    .reset_index()\n",
    ")\n",
    "bar = df_bar.hvplot.bar(x=\"passenger_count\", y=\"count\")\n",
    "\n",
    "# Customizing the plot\n",
    "bar.opts(color=\"green\", width=900, height=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EDA visuals and additional analysis yield the filter logic below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply a list of filter conditions to throw out records with missing or outlier values\n",
    "query_frags = [\n",
    "    \"fare_amount > 1 and fare_amount < 500\",\n",
    "    \"passenger_count > 0 and passenger_count < 6\",\n",
    "    \"pickup_longitude > -75 and pickup_longitude < -73\",\n",
    "    \"dropoff_longitude > -75 and dropoff_longitude < -73\",\n",
    "    \"pickup_latitude > 40 and pickup_latitude < 42\",\n",
    "    \"dropoff_latitude > 40 and dropoff_latitude < 42\",\n",
    "    \"trip_distance > 0 and trip_distance < 500\",\n",
    "    \"not (trip_distance > 50 and fare_amount < 50)\",\n",
    "    \"not (trip_distance < 10 and fare_amount > 300)\",\n",
    "    \"not dropoff_datetime <= pickup_datetime\",\n",
    "]\n",
    "taxi_df = taxi_df.query(\" and \".join(query_frags))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset_index and drop index column\n",
    "taxi_df = taxi_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding Interesting Features\n",
    "\n",
    "We'll use a Euclidean Distance calculation to find total trip distance, and extract additional useful variables from the datetime fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add features\n",
    "taxi_df[\"day\"] = taxi_df[\"pickup_datetime\"].dt.day\n",
    "\n",
    "# calculate the time difference between dropoff and pickup.\n",
    "taxi_df[\"diff\"] = taxi_df[\"dropoff_datetime\"].astype(\"int64\") - taxi_df[\n",
    "    \"pickup_datetime\"\n",
    "].astype(\"int64\")\n",
    "\n",
    "taxi_df[\"pickup_latitude_r\"] = taxi_df[\"pickup_latitude\"] // 0.01 * 0.01\n",
    "taxi_df[\"pickup_longitude_r\"] = taxi_df[\"pickup_longitude\"] // 0.01 * 0.01\n",
    "taxi_df[\"dropoff_latitude_r\"] = taxi_df[\"dropoff_latitude\"] // 0.01 * 0.01\n",
    "taxi_df[\"dropoff_longitude_r\"] = taxi_df[\"dropoff_longitude\"] // 0.01 * 0.01\n",
    "\n",
    "taxi_df = taxi_df.drop(\"pickup_datetime\", axis=1)\n",
    "taxi_df = taxi_df.drop(\"dropoff_datetime\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dlon = taxi_df[\"dropoff_longitude\"] - taxi_df[\"pickup_longitude\"]\n",
    "dlat = taxi_df[\"dropoff_latitude\"] - taxi_df[\"pickup_latitude\"]\n",
    "taxi_df[\"e_distance\"] = dlon * dlon + dlat * dlat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pick a Training Set\n",
    "\n",
    "Let's imagine you're making a trip to New York on the 25th and want to build a model to predict what fare prices will be like the last few days of the month based on the first part of the month. We'll use a query expression to identify the `day` of the month to use to divide the data into train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# since we calculated the h_distance let's drop the trip_distance column, and then do model training with XGB.\n",
    "taxi_df = taxi_df.drop(\"trip_distance\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is the original data partition for train and test sets.\n",
    "X_train = taxi_df.query(\"day < 25\")\n",
    "\n",
    "# create a Y_train ddf with just the target variable\n",
    "Y_train = X_train[[\"fare_amount\"]]\n",
    "# drop the target variable from the training ddf\n",
    "X_train = X_train[X_train.columns.difference([\"fare_amount\"])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the XGBoost Regression Model\n",
    "\n",
    "The wall time output below indicates how long it took to train an XGBoost model over the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time to Train our XGBoost Model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "trained_model = xgb.train(\n",
    "    {\n",
    "        \"learning_rate\": 0.3,\n",
    "        \"max_depth\": 8,\n",
    "        \"objective\": \"reg:squarederror\",\n",
    "        \"subsample\": 0.6,\n",
    "        \"gamma\": 1,\n",
    "        \"silent\": True,\n",
    "        \"verbose_eval\": True,\n",
    "        \"tree_method\": \"hist\",\n",
    "    },\n",
    "    dtrain,\n",
    "    num_boost_round=100,\n",
    "    evals=[(dtrain, \"train\")],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = xgb.plot_importance(\n",
    "    trained_model, height=0.8, max_num_features=10, importance_type=\"gain\"\n",
    ")\n",
    "ax.grid(False, axis=\"y\")\n",
    "ax.set_title(\"Estimated feature importance\")\n",
    "ax.set_xlabel(\"importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How Good is Our Model?\n",
    "\n",
    "Now that we have a trained model, we need to test it with the 25% of records we held out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = taxi_df.query(\"day >= 25\")\n",
    "\n",
    "# Create Y_test with just the fare amount\n",
    "Y_test = X_test[[\"fare_amount\"]]\n",
    "\n",
    "# Drop the fare amount from X_test\n",
    "X_test = X_test[X_test.columns.difference([\"fare_amount\"])]\n",
    "\n",
    "# display test set size\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate predictions on the test set\n",
    "booster = trained_model\n",
    "prediction = pd.Series(booster.predict(xgb.DMatrix(X_test)))\n",
    "prediction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction = prediction.map_partitions(lambda part: cudf.Series(part)).reset_index(drop=True)\n",
    "actual = Y_test[\"fare_amount\"].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate RMSE\n",
    "squared_error = (prediction - actual) ** 2\n",
    "\n",
    "# compute the actual RMSE over the full test set\n",
    "np.sqrt(squared_error.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-Learn Part"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "scaler_x = MinMaxScaler()\n",
    "scaler_y = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_x.fit(X_train)\n",
    "X_train = scaler_x.transform(X_train)\n",
    "X_test = scaler_x.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_y.fit(Y_train.to_numpy().reshape(-1, 1))\n",
    "Y_train = scaler_y.transform(Y_train.to_numpy().reshape(-1, 1)).ravel()\n",
    "Y_test = scaler_y.transform(Y_test.to_numpy().reshape(-1, 1)).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Ridge regression model without  Extension for Scikit-learn*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearnex import unpatch_sklearn\n",
    "\n",
    "unpatch_sklearn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "model_r_s = Ridge(random_state=123)\n",
    "model_r_s.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Ridge regression model with  Extension for Scikit-learn*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearnex import patch_sklearn\n",
    "\n",
    "patch_sklearn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "model_r_p = Ridge(random_state=123)\n",
    "model_r_p.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's look at RMSE metric of models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_p = model_r_p.predict(X_test)\n",
    "y_pred_s = model_r_s.predict(X_test)\n",
    "\n",
    "mse_r_p = mean_squared_error(Y_test, y_pred_p)\n",
    "mse_r_s = mean_squared_error(Y_test, y_pred_s)\n",
    "\n",
    "print(f\"RMSE of patched Ridge: {np.sqrt(mse_r_p)}\")\n",
    "print(f\"RMSE of unpatched Ridge: {np.sqrt(mse_r_s)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the model with Extension for Scikit-learn * learns much faster and has the same rmse."
   ]
  }
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